Comparison and analysis of expertness measure in knowledge sharing among robots

  • Authors:

  • Panrasee Ritthipravat;Thavida Maneewarn;Jeremy Wyatt;Djitt Laowattana

  • Affiliations:

  • FIBO, King Mongkut’s University of Technology Thonburi, Thailand;FIBO, King Mongkut’s University of Technology Thonburi, Thailand;School of Computer Science, University of Birmingham, United Kingdom;FIBO, King Mongkut’s University of Technology Thonburi, Thailand

  • Venue:
  • IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
  • Year:
  • 2006

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Abstract

Robot expertness measures are used to improve learning performance of knowledge sharing techniques. In this paper, several fuzzy Q-learning methods for knowledge sharing i.e. Shared Memory, Weighted Strategy Sharing (WSS) and Adaptive Weighted Strategy Sharing (AdpWSS) are studied. A new measure of expertise based on regret evaluation is proposed. Regret measure takes uncertainty bounds of two best actions, i.e. the greedy action and the second best action into account. Knowledge sharing simulations and experiments on real robots were performed to compare the effectiveness of the three expertness measures i.e. Gradient (G), Average Move (AM) and our proposed measure. The proposed measure exhibited the best performance among the three measures. Moreover, our measure that is applied to the AdpWSS does not require the predefined setting of cooperative time, thus it is more practical to be implemented in real-world problems.